This research explored in depth the integration of a machinist's concept of manufacturing precedence among part features with a complete and explicit graph representation for alternative process plans, A precedence graph that captures the implicit predecessor-successor cost for any directed pair of features observed in the part replaces the state space representation adopted by AI based search strategies. The Hamiltonian path (HP) analogy for a process plan was developed and the Latin multiplication method (LMM) for constrained enumeration of all feasible HP's was implemented in a Turbo Pascal program running on a 486/25 PC. The program was tested using several examples of actual industrial parts obtained from the literature. The experimental results showed that severe constraints are imposed on problem size due to memory limitations imposed by the DOS environment, that the computational burden of using constrained enumeration is heavy and that there is need for using randomly generated alternative feasible plans and iteratively reducing their overall cost. It was observed that the sparseness and connectivity properties of individual precedence graphs improved program execution time. The thrust of this research was bidirectional—incorporating the machinist's heuristic knowledge of manufacturing precedences and implicit costs to make generative CAPP software viable in practice, and systematically generating and ranking several feasible process plans which the planner may not have even considered in the first